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Computes precision@k of the predictions with respect to sparse labels.
tf.contrib.metrics.streaming_sparse_precision_at_k(
predictions, labels, k, class_id=None, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If class_id
is not specified, we calculate precision as the ratio of true
positives (i.e., correct predictions, items in the top k
highest
predictions
that are found in the corresponding row in labels
) to
positives (all top k
predictions
).
If class_id
is specified, we calculate precision by considering only the
rows in the batch for which class_id
is in the top k
highest
predictions
, and computing the fraction of them for which class_id
is
in the corresponding row in labels
.
We expect precision to decrease as k
increases.
streaming_sparse_precision_at_k
creates two local variables,
true_positive_at_<k>
and false_positive_at_<k>
, that are used to compute
the precision@k frequency. This frequency is ultimately returned as
precision_at_<k>
: an idempotent operation that simply divides
true_positive_at_<k>
by total (true_positive_at_<k>
+
false_positive_at_<k>
).
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
precision_at_<k>
. Internally, a top_k
operation computes a Tensor
indicating the top k
predictions
. Set operations applied to top_k
and
labels
calculate the true positives and false positives weighted by
weights
. Then update_op
increments true_positive_at_<k>
and
false_positive_at_<k>
using these values.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args | |
---|---|
predictions
|
Float Tensor with shape [D1, ... DN, num_classes] where N >=
|
labels
|
int64 Tensor or SparseTensor with shape [D1, ... DN,
num_labels], where N >= 1 and num_labels is the number of target classes
for the associated prediction. Commonly, N=1 and labels has shape
[batch_size, num_labels]. [D1, ... DN] must match predictions . Values
should be in range [0, num_classes), where num_classes is the last
dimension of predictions . Values outside this range are ignored.
|
k
|
Integer, k for @k metric. |
class_id
|
Integer class ID for which we want binary metrics. This should be
in range [0, num_classes], where num_classes is the last dimension of
predictions . If class_id is outside this range, the method returns
NAN.
|
weights
|
Tensor whose rank is either 0, or n-1, where n is the rank of
labels . If the latter, it must be broadcastable to labels (i.e., all
dimensions must be either 1 , or the same as the corresponding labels
dimension).
|
metrics_collections
|
An optional list of collections that values should be added to. |
updates_collections
|
An optional list of collections that updates should be added to. |
name
|
Name of new update operation, and namespace for other dependent ops. |
Returns | |
---|---|
precision
|
Scalar float64 Tensor with the value of true_positives
divided by the sum of true_positives and false_positives .
|
update_op
|
Operation that increments true_positives and
false_positives variables appropriately, and whose value matches
precision .
|
Raises | |
---|---|
ValueError
|
If weights is not None and its shape doesn't match
predictions , or if either metrics_collections or updates_collections
are not a list or tuple.
|